We evaluated our layers on two datasets : CIFAR10[1]} and CIFAR100[1]}.
CIFAR10 and CIFAR100 are two data set composed of 50 000 training RGB images and 10 000 RGB test images. The Images are of size \(32 \times 32\) . CIFAR10 has 10 classes, while CIFAR100 has 100 classes.
To train the DNN, we used the cross-entropy loss and reported the classification error.
To train the DNN, we use the same experimental protocol as NAO use for CIFAR 10. During the architecture search, we use a small network with \(B=5\)  (number of nodes), \(N=3\)  (number of normal cells), \(F=8\)  (number of channels) and search the architecture for four iterations; each of them is composed of 50 epochs. After the best cell architectures are found, we increase the architecture with \(B=5\) , \(N=6\) , \(F=36\)  and optimize the weight of the DNNs for 600 epochs. The batch size for the two-step is 128.
The results in Table REF  are the mean of 3 seeds. The mean error of our architecture with the morphological layer is 2.65%, which is below 2.93% of NAO[3]}. We tried to replace the morphological dilation with erosion and saw that the result decreased.
Hence we noticed on CIFAR 10 that the morphological dilation layer improves the performances of the DNN. We also noticed from Figure REF  that NAO used this operation since it is present in 3 edges out of 15 for the Normal Cell and 4 out of 15 for the Reduction Cell. Morphological operations seem to bring information not present by traditional convolutional that can help to improve performances.
